In the previous blog post we talked about the social context of our decision-making processes. We used the example from the healthcare domain to show that decision making these days rarely occurs in isolation and that technical solutions aimed at supporting these processes need to become essentially social. In this post, we will take a step further and talk a bit about designing data analysis solutions to be effective and useful in social and business contexts. These contexts are dynamic and usually more complex that they might seem. They include multiple elements, roles, types of relationships and structures; can be designed and constructed, or grown organically; can exist continuously in background (everybody has multiple ones) or have a short lifespan tied to a specific purpose or situation. Such diverse characteristics can result in completely different functional requirements, what means for data analysis solutions that they need to be very flexible and adaptable.

Data analysis in social context is about sharing, but not only of data and results, but also of efforts, skills, experiences, and - probably the most important here – different points of view. There are some technical elements that are common in all such solutions, including efficient data exchange that enables natural and smooth interactions, navigation through complex data spaces, and management of relationships (sometimes completely new types). We can also try to identify some higher-level principles that help with building effective and useful solutions for various social contexts:

Focus is on users as the centers of social contexts. This starts with a personal user experience and need for understanding individual requirements and preferences. But it can quickly get even more difficult, if we have multiple users with incompatible or conflicting goals. There is a need for clarity (do these agents really operate according to my priorities?) and transparency (who can access data or control the process?). In many situations, analysis decision support may include defining contract-based goals and rules of data analysis efforts (e.g. solving a specific problem).
Data analysis processes are distributed efforts. The scope of data analysis in social context expands from an individual, into groups, communities and eventually societies. This requires effective interactions between multiple participants, both human and agents, across shared data spaces. Here the requirements can be very different and a solution must support various scenarios covering cooperation, negotiations or competition. There can be also the challenges of integrating individual experiences (each with possibly different presentation) into consistent group communication system.
Data analysis process is usually part of a bigger system. Problems and contexts are unique; types of tasks, best practices, patterns and challenges are more general. A data analysis process can benefit from similar external projects (e.g. for population big picture) and contribute to them (with anonymized data). There are opportunities for sharing competencies, efforts and solutions even externally, in open, research or commercial frameworks. However, integration scenarios require very clear consistent rules and transparency regarding privacy, security or ownership of information.
Intelligent agents can be essential participants of data analysis. Interactions during analysis or decision making process can take place in networks of human and non-human actors. Intelligent agents can be interactive participants, sharing information with users or performing specific tasks per request. They can also operate in the background, monitoring actions, conversations or external events, and acting when it is needed or useful. In group scenarios, they may take special roles, like optimizing of efforts, balancing the structure, or mediating with odd or even number of agents.

Let’s take a quick look at that last point, as it seems to be the clearest illustration of relationships between technology and social contexts. We will reuse the example from the healthcare domain, introduced in our previous blog post, which shows relationships between a patient’s context (family and friends) and the physician’s context (professional medical network). Figure 1 presents that structure, with the addition of new connections involving intelligent agents, some interactive and others operating in the background. Interactive agents can provide direct assistance and support to patients, their friends and families, along with connections to the medical side, where different types of agents can help with coordination of efforts and collaboration in medical analysis. Background agents can enable various scenarios, like continuous remote monitoring (not only in the scope of physiological metrics), integration with population efforts (connecting physicians working on similar cases) or automatic documentation of decision processes.

Figure 1. An example of a social structure in healthcare combining humans and intelligent agents

Similar scenarios may seem distant, but they are already here, although usually in simpler configurations with a bot or a digital assistant as front-end to a realm of specific services. In the scope of data analysis, including a social context is a natural consequence of focusing on the user’s goals, needs and preferences. In our framework, this focus starts with personalized user experiences based on individual choices and activities. For groups scenarios, it is expanded to also include the user’s role, relationships and characteristics of a social or business context. At this point data analysis is no longer only about sharing, but also about communication and conversations embedded in a shared data space. Intelligent agents can fit in such spaces very naturally and become the key participants. An agent can interact with users, change their behaviors or even become an active driver of interactions between different users and agents. The result is a completely new social structure - technology is not only capable of adopting to a social context, but may shape it or, in some cases, construct it.

Human elements will long remain fundamental in solving real problems and there are great opportunities for solutions facilitating cooperation in complex scenarios. There are situations, where enabling efficient cooperation may actually be more important than selecting the right algorithms and analysis techniques. The data analysis solutions must however be designed for social and business contexts, with clear rules and transparency, always close to users and actively addressing challenges like possible incompatibilities in priorities between individuals or an individual and a group. Including social context in data analysis is becoming however unavoidable, due in part to the increasing popularity of conversation-based interactions. And with the application of intelligent agents, social context is added to all data analysis projects, even those conducted by a single user.